Crystal Structure Landscape of Diarylethene-Based Crystalline Solids: A Comprehensive CSD Analysis DOI
Xiaotong Zhang, T. B. Mitchell, Jason B. Benedict

и другие.

Crystal Growth & Design, Год журнала: 2024, Номер 24(15), С. 6284 - 6291

Опубликована: Июль 23, 2024

Diarylethenes (DAEs) are an exciting class of stimulus-responsive organic molecules that exhibit electrocyclization reactions upon exposure to light, heat, or other stimuli. The rational design DAE-based crystalline materials is, however, complicated by the presence DAE atropisomers, only one which is photoactive. Data mining CSD produced 1349 unique molecular structures were subsequently analyzed according selected chemical and geometric attributes. Additional analyses performed on 1078 dithienylethene (DTE) structures-the largest subgroup within ensemble. crystal structure landscape, based

Язык: Английский

Optimizing Drug Development: Harnessing the Sustainability of Pharmaceutical Cocrystals DOI
Srinivasulu Aitipamula, Geetha Bolla

Molecular Pharmaceutics, Год журнала: 2024, Номер 21(7), С. 3121 - 3143

Опубликована: Май 30, 2024

Environmental impacts of the industrial revolution necessitate adoption sustainable practices in all areas development. The pharmaceutical industry faces increasing pressure to minimize its ecological footprint due significant contribution environmental pollution. Over past two decades, cocrystals have received immense popularity their ability optimize critical attributes active ingredients and presented an avenue bring improved drug products market. This review explores potential as ecofriendly alternative traditional solid forms, offering a approach From reducing number required doses improving stability actives, from eliminating synthetic operations using pharmaceutically approved chemicals, use continuous solvent-free manufacturing methods leveraging published data on safety toxicology, cocrystallization contributes sustainability latest trends suggest promising role bringing novel medicines market, which has been further fuelled by recent guidance major regulatory agencies.

Язык: Английский

Процитировано

7

Advanced Feature Analysis for Enhancing Cocrystal Prediction DOI Creative Commons

Alessandro Cossard,

Chiara Sabena,

Gianluca Bianchini

и другие.

Chemometrics and Intelligent Laboratory Systems, Год журнала: 2025, Номер unknown, С. 105318 - 105318

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1

Prioritizing Computational Cocrystal Prediction Methods for Experimental Researchers: A Review to Find Efficient, Cost-Effective, and User-Friendly Approaches DOI Open Access
Beáta Lemli, Szilárd Pál, Ala’ Salem

и другие.

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(22), С. 12045 - 12045

Опубликована: Ноя. 9, 2024

Pharmaceutical cocrystals offer a versatile approach to enhancing the properties of drug compounds, making them an important tool in formulation and development by improving therapeutic performance patient experience pharmaceutical products. The prediction involves using computational theoretical methods identify potential cocrystal formers understand interactions between active ingredient coformers. This process aims predict whether two or more molecules can form stable structure before performing experimental synthesis, thus saving time resources. In this review, commonly used are first overviewed then evaluated based on three criteria: efficiency, cost-effectiveness, user-friendliness. Based these considerations, we suggest researchers without strong experiences which tools should be tested as step workflow rational design cocrystals. However, optimal choice depends specific needs resources, combining from different categories powerful approach.

Язык: Английский

Процитировано

4

Exploring Coformer Substitution in Cocrystallization: Griseofulvin and Phenol Derivatives DOI Creative Commons

Janine Lässer,

Doris E. Braun

Crystal Growth & Design, Год журнала: 2025, Номер 25(5), С. 1688 - 1707

Опубликована: Фев. 14, 2025

This study investigates the cocrystallization of griseofulvin with phenolic coformers, highlighting its feasibility and variability. In addition to previously reported cocrystal 4-t-butylphenol (1:1), experimental screening identified three new cocrystals: phenol (2:5), 4-t-amylphenol 2,4,6-trichlorophenol (2:3). Phenols carbon substituents in ortho or meta positions failed form cocrystals, likely due steric hindrance electron-donating effects. contrast, phenols chlorine substituents, particularly para positions, demonstrated enhanced potential, driven by electron-withdrawing effects that promote hydrogen bonding. The 2:5 required optimized conditions for isolation exhibited instability under ambient coformer sublimation, a tendency also observed other cocrystals. While challenging, sublimation facilitated determination stoichiometric ratios, which varied from 1:1 2:3 2:5. Furthermore, this provides data set cocrystal-forming noncocrystal-forming combinations as rigorous test case virtual prediction. Among tested methods, crystal structure prediction proved most reliable, identifying all and, together powder X-ray diffraction, offering insights into structures. Future integration CSP machine learning could accelerate speed accommodate broader range ratios. Overall, work highlights complexity potential cocrystallization.

Язык: Английский

Процитировано

0

Cocry-pred: A Dynamic Resource Propagation Method for Cocrystal Prediction DOI
Wenxiang Song, Ren Peng, Hongbo Yu

и другие.

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

Drug cocrystallization is a powerful strategy to enhance drug properties by modifying their physicochemical characteristics without altering chemical structure. However, the identification of suitable coformers remains challenging and resource-intensive task. To streamline this process, we developed novel cocrystal prediction model, Cocry-pred, which utilizes Network-Based Inference (NBI) algorithm─a dynamic resource propagation method─to recommend for target molecules based on topological data from network molecular substructure information. We evaluated impact 13 types fingerprints different numbers rounds model performance. Additionally, achieve optimal performance, introduced three key hyperparameters─α (node weights), β (edge weights) γ (penalty high-degree nodes)─to balance influence various factors within composite network. The best performance Cocry-pred achieved an impressive AUC 0.885 RS 0.108. validate reliability employed it predict potential Apatinib. Subsequently, seven Apatinib cocrystals were then synthesized experimentally, among single-crystal structures obtained two cocrystals. This advancement highlights as tool, offering significant improvements in efficiency providing valuable insights screening design.

Язык: Английский

Процитировано

0

Virtual Screening of Pharmaceutical Cocrystals Using Machine Learning Algorithms DOI

Roshni Jayachandiran,

Suneesh Jacob Akkarapakam,

T. Karthick

и другие.

Crystal Growth & Design, Год журнала: 2025, Номер unknown

Опубликована: Апрель 25, 2025

Язык: Английский

Процитировано

0

Engineering Cocrystals of Naringenin: New Solid Forms and Assessing Predictive Tools for Coformer Selection DOI Creative Commons
Sanika Jadhav, Matthew J. Speranza, Aaron J. Nessler

и другие.

Crystal Growth & Design, Год журнала: 2025, Номер unknown

Опубликована: Апрель 25, 2025

Язык: Английский

Процитировано

0

Ammonium Organosulfonates as Machine Learning-Driven “Crystallization Cocktails” for the Structure Determination of Liquid Molecules DOI
Anastasia A. Danshina,

Ivan Zlobin,

С. Е. Соловьева

и другие.

Crystal Growth & Design, Год журнала: 2025, Номер unknown

Опубликована: Май 29, 2025

Язык: Английский

Процитировано

0

Computational predictions of cocrystal formation: A benchmark study of 28 assemblies comparing five methods from high‐throughput to advanced models DOI Creative Commons
Robert B. Fox,

Joaquín Klug,

Damien Thompson

и другие.

Journal of Computational Chemistry, Год журнала: 2024, Номер 45(29), С. 2465 - 2475

Опубликована: Июль 3, 2024

Abstract Cocrystals are assemblies of more than one type molecule stabilized through noncovalent interactions. They promising materials for improved drug formulation in which the stability, solubility, or biocompatibility active pharmaceutical ingredient (API) is by including a coformer. In this work, range density functional theory (DFT) and tight binding (DFTB) models systematically compared their ability to predict lattice enthalpy broad existing pharmaceutically relevant cocrystals. These from cocrystals containing model compounds 4,4′‐bipyridine oxalic acid those with well benchmarked APIs aspirin paracetamol, all tested large set alternative coformers. For simple cocrystals, there general consensus calculated different DFT models. API coformers predictions depend strongly on model. The significantly lighter DFTB unrealistic values even

Язык: Английский

Процитировано

2

A Comprehensive Review on Theoretical Screening Methods for Pharmaceutical Cocrystals DOI

J. Roshni,

T. Karthick

Journal of Molecular Structure, Год журнала: 2024, Номер 1321, С. 139868 - 139868

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

2